Abstract Effective risk allocation is essential for the success of transport infrastructure public–private partnership (TI-PPP) projects. However, inherent features of these projects, including multiple entities, multiple tasks, and dynamicity, complicate the generation of efficient risk allocation solutions. This study develops a feature-driven modeling methodology for dynamic risk allocation involving multiple entities and full task coverage. A dedicated risk allocation protocol is designed, consisting of four modules of risk identification, scenario assumption, model construction, and solution visualization. Using proposed assumptions and multisource data, a multistage dynamic tripartite model is proposed that combines rule definition, algorithm design, and Shapley value method extension to allocate the identified risks. The generated solutions are visualized through a risk allocation matrix. Model applicability is demonstrated via a case study, with multifaceted validations confirming its effectiveness, reliability, and robustness. The results enable entities to intuitively clarify their respective undertaken risks and allocation proportions at any time, while also providing case-based management implications. Theoretically, this study offers a comprehensive paradigm spanning methodology development to validation for risk allocation research. Practically, it delivers operational guidance and policy references for risk allocation practices.

Resilience is a crucial benchmark in characterizing the comprehensive capability of the emergency material support system (EMSS) to respond to major risk events. Given the involvement of multiple stakeholders, multiple stages and dynamic evolution, EMSS resilience assessment remains a challenge. Therefore, we attempt to develop a novel data-intelligence-driven three-stage dynamic model based on multi-source text data and multi-expert knowledge. In Stage 1, a large language models-enhanced named entity recognition model is proposed to extract and analyze EMSS risk events, providing a foundational dataset for scenario construction. In Stage 2, an ontology-based scenario construction model is proposed to abstract risk events into ontological concepts, providing a feature reference for the hierarchical system of assessment criteria. In Stage 3, a feature-matching assessment model is proposed to quantify the profile of EMSS resilience, where the uncertainty and variability in experts' perceptions of resilience feature are addressed. Subsequently, the model effectiveness is demonstrated in a case study, in which the key criteria and improvement paths for EMSS resilience are identified. This study provides a holistic solution and efficient methodology for EMSS resilience assessment, offering significant insights into a multifaceted recognition of EMSS resilience to risk scenarios.

Urban resilience assessment (URA) is challenging because of urban system complexity and dynamic resilience variability. This paper develops a URA method with comprehensive feature consideration and integrated data use and then constructs a hierarchical URA criteria system. Subsequently, a two-stage integrated dynamic assessment method based on multisource data is presented, wherein the subjective-objective combination weights are determined, and the dimensional and overall urban resilience (UR) indexes are constructed. The applicability and superiority of the proposed method to existing methods are verified using a case study in Beijing. The results showed that UR in Beijing has improved substantially in 2016-2020; social and ecological dimensions are important for UR improvement; and synergies exist between different UR dimensions, which are crucial for resilient urban development. This study provides a systematic solution for URA that features a dynamic perspective, multisource data utilization, and subjective-objective combination weights, enhancing URA comprehensiveness and accuracy.

With the increasing emphasis on research institutions in China, there is a growing imperative to evaluate the efficacy of resource allocation in science and technology (S&T) investments. However, limited research has been conducted on the efficacy of resource allocation in research institutions while considering decision makers’ risk preference behavior. In order to address this issue, the present study employs a prospective cross-efficiency evaluation model that incorporates decision makers’ risk preferences. This model is utilized to assess the efficiency of S&T resource allocation in research institutions across 13 industries in China. After conducting empirical analysis, it has been observed that decision makers’ risk preferences significantly influence the efficiency outcomes of S&T resource allocation in research institutions. Finally, this study proposes appropriate measures and recommendations for S&T resource allocation in research institutions.

Critical infrastructures (CIs) play an important role in urban economic development and the maintenance of social stability. In recent years, with the increase in natural and man-made risk events affecting CI operations, CI risk management is facing severe challenges and gradually transitioning to CI resilience management. However, the connotation of “resilience - risk” for CIs is still not clearly conceptualized. To address this problem, an exploratory theoretical framework is constructed, including three stages of system resilience identification, resilience impact analysis and connotation element extraction. Subsequently, based on the constructed framework, probability, consequence, and resilience-based scenario that align with the resilience characteristics and impact analysis results are extracted as connotation elements to define a triplet spatial-temporal evolution function of “resilience - risk”. This study can provide a theoretical reference for better understanding the relationship between CI resilience and CI risk.

Critical Infrastructures (CIs) are exposed to various risks, which hinder their successful operation and induce great losses. Due to the interdependency between risks and that between CIs, the identification of the most prominent risks becomes complex and challenging. However, existing studies rarely considered the dual interdependency of risks and CIs. This study proposes a double-layer network for multiple interdependent risks and CIs. The Design Structure Matrix (DSM) and Restart Random Walk (RRW) algorithm are combined to determine the impact of risks on CIs by incorporating the strength of dual interdependency. The LeaderRank algorithm is then used to rank these risk factors and an illustrative example is given to validate the model. The proposed model and algorithms can systematically quantify complex interdependencies embedded in the operation of CIs susceptible to multiple risks, and provide decision-makers with evidence to prioritize risks.

In the face of uncertainties and instabilities in the internal and external environments, China's development is facing challenges that require higher emergency supplies guarantee capabilities. Information construction is an effective means to enhance these capabilities. Through literature research, the information construction experiences of two disaster-prone countries, the United States and Japan, were compared and summarized, and combined with China's practical development, it was concluded that the standardization of information construction for emergency supplies guarantee systems should be accelerated and the modernization of emergency logistics systems should be promoted.

National resilience is a consensus benchmark to characterize the ability of disaster resistance of a country. The occurrence of various disasters and the ravages of COVID-19 have created urgent needs in assessing and improving the national resilience of countries, especially for countries along the Belt and Road (i.e., B & R countries) with multiple disasters with high frequency and great losses. To accurately depict the national resilience profile, a three-dimensional assessment model based on multi-source data is proposed, where the diversity of losses, fusion utilization of disaster and macro-indicator data, and several refined elements are involved. Using the proposed assessment model, the national resilience of 64 B & R countries is clarified based on more than 13,000 records involving 17 types of disasters and 5 macro-indicators. However, their assessment results are not optimistic, the dimensional resilience are generally trend-synchronized and individual difference in a single dimension, and approximately one-half of countries do not obtain resilience growth over time. To further explore the applicable solutions for national resilience improvement, a coefficient-adjusted stepwise regression model with 20 macro-indicator regressors is developed based on more than 19,000 records. This study provides the quantified model support and a solution reference for national resilience assessment and improvement, which contributes to addressing the global national resilience deficit and promoting the high -quality development of B & R construction.

Representing disaster scenarios and evaluating the emergency material support system (EMSS) is crucial to enhance emergency material support capabilities. Current representation methods for EMSS mostly focused on the task response procedure during emergencies, rarely involved the response process analysis for disaster scenarios. This study utilizes an ontological method to construct a representation of risk response scenarios for the EMSS. It can be achieved by representing scenario feature elements, scenario structure elements, scenario constraint elements, and scenario attribute elements through the four dimensions. The scenario representation can generate different setting schemes for EMSS. An example scenario was presented based on the measures implemented by the Chinese government during the COVID-19 epidemic's closure of Wuhan. The findings of this research can provide valuable support for making risk-informed decisions regarding EMSS.

能源行业上市公司作为重要的经济主体,在能源金融一体化深度发展的当下,其股价联动呈现出复杂的网络特征.文章从股票收益、波动与投资者情绪的三维复合视角,构建中国新能源和传统能源上市公司股价关联的最小生成树网络,识别了新冠疫情和双碳政策对中国能源上市公司股价关联网络特征的影响.实证结果表明:1)情绪网络的传导效率最高且易于在两类公司之间传导,收益网络的传导效率最低且易于在同类公司内部传导;2)新冠疫情期间,各类网络传导效率均增加;3)双碳政策后,两类公司间关联减弱,三维网络间相似度提升.文章有助于辅助监管机构监测系统性重要公司、指导投资者结合多维网络特征进行风险管理.

The emergency supplies security system (ESSS) plays a vital role in preventing major disasters and ensuring people's safety. Resilience is an important benchmark to characterize the ability of disaster resistance of the ESSS. However, the ESSS complexity and resilience dynamic evolution make ESSS resilience assessment challenging. This study develops a hierarchical ESSS resilience assessment system comprising three dimensions and twelve criteria, and proposes a dynamic resilience assessment model based on a matter-element extension method. Subsequently, a case study is conducted to demonstrate the applicability of the proposed model. The assessment results indicate that the ESSS resilience in the sample cities has improved over the past five years. However, there are still some shortcomings such as low response rates and high operating costs. This study provides a valuable criteria reference and effective model support for ESSS resilience assessment, which is beneficial for decision makers to clarify the resilience profile, identify the weakness and promote resilience improvement.

An individual's reluctance to reporting project risks has become a significant issue that hinders project success. To better understand this behavior and facilitate risk reporting decision-making, this paper first clarifies the project implementation entities to project members (i.e., whistle-blowers) and project managers (i.e., whistle-hearers). Then, an evolutionary game analysis is conducted to investigate the behavioral biases of each entity and their behavioral interactions. Five equilibrium solutions of risk reporting strategies have been obtained through the replicator equations. Subsequently, a system dynamics model is constructed to intuitively demonstrate the dynamism and complex feedback structures of the whistle-blowing processes. Different scenarios have been proposed, which can further simulate the evolutionary patterns of risk reporting strategies in multiple contexts.

This paper presents an optimization model to complete the missing data in the energy matchup matrix which compares different players’ energy. The optimization model searched for the solutions that can make the eigenvalue of the energy matrix the biggest that should be the principle one. To test the model, we built a Bayesian matrix of energy matchup which mixes the judgments of energy in a matchup between players given by experts and the statistical data gotten from each game in different positions. The proposed method can complete the probabilities given by the Bayesian matrix. Finally, an implementation shows the effectiveness and rationality of the model.

从单一模式向多元化集成模式转变已成为新形势下拓宽交通基础设施建设融资渠道的必然选择,在规避单一模式局限、形成多元模式优势合力的同时,也加剧了融资不确定性和复杂性,从而对融资风险识别提出了更高的要求.综合考虑多元化集成模式下交通基础设施建设融资风险的多源性、关联性、模糊性、随机性等典型表征,设计了与典型表征相匹配的融资风险识别框架,并构建了融资风险识别两阶段模型.在该模型中,明确了融资风险因素确定流程和维度划分规则,并提出了基于随机二元语义DEMATEL的风险因素关联分析方法进行融资风险因素的研判与诊断.最后,以"PPP+ABS"模式下大成西黄河大桥收益权计划为例开展了计算实验,验证了所构建模型的有效性,并给出了融资风险应对与化解的启示与建议.研究成果能够为相关利益主体明晰融资风险因素构成、研判融资风险因素影响力、诊断融资风险因素可控性、有效应对与化解融资风险提供系统性解决方案.

Effective risk allocation is crucial for the success of transport infrastructure public-private partnership (PPP) projects. However, the inherent features of multiple entities, multiple tasks, and dynamicity make it highly challenging to obtain a risk allocation solution. This study aims to develop a scenario-driven modeling methodology for dynamic project risk allocation with multi-entity involvement and multi-task whole-coverage. First, a resolution framework for the scenario-driven risk allocation modeling is designed, in which risk identification and scenario assumptions are elaborated, and a four-stage resolution framework is constructed to fit the complex dynamic scenario. Subsequently, a multistage dynamic tripartite model is proposed based on rule definition, algorithm design, Shapley method extension, and visualized matrix construction. Furthermore, a case study is conducted to verify the applicability of the proposed model. It indicates that the obtained risk allocation solution is beneficial for entities to clarify their respective undertaken risks and specific allocation proportions at any time. This study provides an effective problem-solving methodology for risk allocation based on a comprehensive scenario with multi-feature consideration, a clear framework with whole-process guidance, an efficient model with quick response and visualized presentation, and a beneficial application attempt with reference implications.

Urban resilience reflects the ability of cities to resist, absorb, adapt and recover from danger in a timely and efficient manner, which is critical to the normal operations of cities and the daily lives of citizens. An increasing number of cities have included resilience in their city planning. Thus, conducting the resilience evaluation is necessary for urban resilience improvement and enhancement. This paper proposed a MACBETH-based method for urban resilience evaluation, in which a multi-dimensional evaluation system including four dimensions and sixteen criteria is established, and the main procedure was presented to determine the overall and dimensional resilience indices. The proposed method was applied to evaluate Beijing's urban resilience. The results shown that Beijing's resilience improved from 2016 to 2020, despite the impact of the COVID-19 pandemic in 2020.

Bayesian learning has been successfully used in many fields to make decision or sense the outcome of causes or influences of events. However, the relations between causes and observed events are more complicated than the Bayesian inference can learn, like prediction in some psychology experiments, which should consider human experience and the interdependence of the events. This paper raises Bayesian learning to intelligent learning by putting events in a network to analysis the complicated criterions and interdependences among events with the individual probability judgments. Finally, the results of experiment used by intelligent learning illustrate more complex relations than Bayesian learning.

科技创新发展指数研究是源于实践、用于实践、高于实践的典型智库研究,但已有研究存在理论性不足、视角单一等问题,不利于保证研究过程的科学性和研究结果的准确性.智库双螺旋法兼具系统性思维、全流程指导和操作性思路的典型特征,为有效解决上述问题提供了新的思路.文章基于智库双螺旋法,建立了一套从内涵解析、指标构建、指标赋权到指标测度的全流程系统性动态化研究框架,并以秦创原科技创新发展指数研究为例开展实证研究,得到了有价值的研究启示和思考发现.研究过程充分验证了智库双螺旋法对于智库研究的科学指导作用,研究结果为研判秦创原科技创新发展态势及其对陕西省高质量发展的贡献提供了重要的定量支撑和决策参考.

国家风险是我国企业海外投资优先考虑的外部因素,其构成的多维度性、不同维度风险之间关联的客观存在性以及企业决策者差异化的偏好等特点,加剧了海外投资国家风险评估的难度.本文引入风险关联和决策者偏好,提出一种二元语义DEMATEL法、基尼系数客观赋权法与VIKOR法相结合的海外投资国家风险评估方法来量化风险关联,并从主客观集成视角确定风险评估指标权重,以期得到不同决策机制下的投资风险国别排序.随即,通过采集"一带一路"沿线63个国家的数据开展实证分析,验证所提出方法的有效性,并对结果进行分析与讨论.研究结果能够为海外投资区位选择提供必要的决策支持,也能够对我国企业海外投资风险防范、推动共建"一带一路"高质量发展有所裨益.

技术路线图是一种高效的战略管理与规划方法.以技术路线图为依托,识别产业发展的关键技术、助力区域科技创新规划部署,是提高区域自主创新能力、加速实现科技支撑引领区域高质量发展的重要途径.文章阐述了技术路线图的概念与特征、作用和绘制流程,详细说明了技术路线图在青海省"十四五"科技创新规划编制中重点领域的应用实践过程和结果,并给出了技术路线图支撑区域科技创新规划编制的研究范式,能够为其他区域、产业的科技创新规划编制提供有价值的借鉴和重要的参考.